Measuring image characteristics of output from a digital printer

ABSTRACT

Measuring the printed image characteristics of printed output from a digital printer by sending test pattern data to a digital printer and generating a printed image of the test pattern data. The printed image is scanned, and digital pattern data is output. The digital pattern data is analyzed to generate one or more quantitative ratings with respect to one or more printed image characteristics.

BACKGROUND OF THE INVENTION

[0001] This invention relates to measuring image characteristics ofprinted output from a digital printer.

[0002] A digital printer receives electronic digital input data in theform of a bitmap (in which there is one bit per pixel), and outputs aprinted sheet. The input data are typically received serially in araster pattern and are used to control the deposit of toner from a drumto a sheet of paper.

[0003] Many factors can cause the print quality of a digital printer todeteriorate over time. Traditionally, assessments of this degradationhave been done subjectively, as a user tries to match the printer'simage quality with his or her subjective perception of ideal output.

[0004] Generally, printed sheets are manually inspected to determinequality. The user generates a test page and judges its quality bymentally recalling the appearance of the test page when the printer wasnew or last serviced. The user can also compare the test page to samplesin a “limits” book, which defines the lower bounds for standard printeroutput. An actual printed image may have defects such as white pinholesin black areas, smears, or blurry edges. Some defects may be acceptableand some may fall below an acceptable quality level. Based on manualinspection, the user rejects printed sheets or makes adjustments to theprinter.

[0005] A technician performing preventive maintenance may obtainreadings of the print density uniformity by using a densitometer, whichmeasures light reflected from a page. These readings only partiallycorrelate with human judgments of print quality. Therefore, unless thereis a dramatic shift in quality, or a noticeable defect such asstreaking, users have difficulty detecting subtle or gradual changes inquality. With color output, detecting changes is even more difficultsince the eye is poor at identifying absolute colors.

SUMMARY OF THE INVENTION

[0006] In one aspect, the invention features, in general, measuringimage characteristics of printed output from a digital printer bysending test pattern data to the digital printer, generating a printedimage of the test pattern data at the digital printer, scanning theprinted image to obtain digital pattern data, and analyzing the digitalpattern data of-the printed image. The test pattern that is printedincludes target objects designed to reveal specific printed imagecharacteristics, and the analysis of the data from scanning the printedimage includes the generation of one or more quantitative ratings withrespect to printed image characteristics.

[0007] In other aspects, the invention features computer programs, printsystems, digital printers, test instruments, and services that carry outthe method of measuring print image characteristics just described.

[0008] Embodiments of the invention may include one or more of thefollowing features. The quantitative ratings can include measurements ofthe black and white densities, uniformity, edge sharpness, resolution,and positional accuracy of the target objects. The measurements alsodetect defects, such as streaks and smears. Landmarks in the testpattern are used to correct for scanner error.

[0009] One or more quality ratings can be generated from themeasurements. Using these ratings, one can automatically monitor andadjust the print image characteristics of a digital printer so that theprinter is within allowable margins. For example, one can use themeasurements to adjust print engine related parameters such as power andduration of the laser, and charging current. The method is applicable toblack and white as well as color printing applications.

[0010] Other aspects and advantages of the invention will be apparentfrom the drawings taken together with the accompanying description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 is a diagram of a print analyzer system in accordance withthe invention;

[0012]FIG. 2 is an illustration of test pattern data;

[0013]FIG. 3A is a diagram of the print quality analyzer components;

[0014]FIG. 3B is a diagram outlining the series of tests conducted bythe print analyzer system of FIG. 1;

[0015]FIG. 4A is an illustration of pixel clusters;

[0016]FIG. 4B is a flow chart of the print density uniformity metric;

[0017]FIG. 4C is a representation of pixel density across a targetillustrating a streak condition;

[0018]FIG. 4D is a representation of pixel density across a targetillustrating a smear condition;

[0019]FIG. 5 is a diagram of a printer having a built-in scanner; and

[0020]FIG. 6 is a diagram of a computer system having a qualityanalyzer.

DETAILED DESCRIPTION

[0021] Referring to FIG. 1, print analyzer system 10 is shown. Bitmap 12represents a raster image for a test pattern that is input to a blackand white digital printer 14 being evaluated. Each bit defines whether acorresponding pixel is black or white. The output of digital printer 14is printed image 16 on a sheet of paper. Bitmap 12 may be produced byscanning a printed test pattern to generate a raster image, may begenerated electronically in the first instance, or may be based on acombination of inputs.

[0022] To measure the image characteristics of output from digitalprinter 14, printed image 16 is scanned by scanner 18, and the outputfrom scanner 18, namely electronic digital pattern data 20, is analyzedfor print image characteristics at print image analyzer 22, which isimplemented in a personal computer. Scanning is done by feeding printedimage 16 into a high-resolution scanner or by having digital printer 14route printed image 16 directly to a built-in scanner (see FIG. 5).Scanner 18 does not have to operate at the rated printer speed since itis only used to perform image measurements. However, scanner 18 musthave sufficient spatial resolution to capture the details in printedimage 16. In general, scanner 18 should have at least twice theresolution, both horizontally and vertically, as printer 14 beingevaluated. Digital printer 14 can also route a test page to scanner 18during normal high speed printing operations, thereby testing a printedsheet at a random or pre-defined interval in a printer's output cycle.

[0023] Scanner 18 outputs digital pattern data 20, which is a highresolution digital representation of printed image 16. Digital patterndata 20 is a rectangular array of numerical values stored in memory.There are 8 bits per scanner pixel; these numerical values range from 0,which denotes pure black, to 255, which denotes pure white. The data forthe image on printed image 16 are stored in a buffer thereafter accessedby print image analyzer 22. Values between 0 and 255 are variations frompure black to pure white. Print image analyzer 22 evaluates the printimage characteristics of the scanned image by conducting a series oftests on digital pattern data 20. Based on the tests, print imageanalyzer 22 generates quality ratings and/or control signals to makeadjustments to digital printer 14. For example, print image analyzer 22can indicate that adjustments should be made to bias voltages, surfacecharge levels, laser exposure levels, and printer speeds in digitalprinter 14. If an adjustment can be made using software (e.g., bysetting parameters in control memory for digital printer 14), printimage analyzer 22 can send commands to adjust the appropriateparameters, measure the resulting quality, and then iteratively adjustthem.

[0024] Referring to FIG. 2, test image 16 includes target objectsdesigned to reveal specific imaging characteristics of digital printer14. The upper portion 15 of printed image 16 contains samples ofdifferent text, graphics, and image patterns and is intended for visualquality ranking (not shown). Approximately the lower third of printedimage 16 contains target objects 16 a-f that are analyzed without humanintervention. Target objects 16 a-f are test patterns designed toindicate image defects and are analyzed to evaluate print imagecharacteristics. They are shown diagrammatically on FIG. 2 and describedin more detail below. In the corners of printed image 16 are landmarks26 a, 26 b, 26 c. Before evaluating the image, print image analyzer 22has a landmark registration process to compensate for imperfect scannersand to identify the expected locations in the buffer for target objects16 a-f.

[0025] To compensate for imperfect scanners, the print analyzer systemaligns digital pattern data 20 with a template to account for anyskewing or margin deviations between digital printer 14 and scanner 18.Print image analyzer 22 stores a template in memory that represents theideal location of pixels within landmark 26 a and uses a signalprocessing/statistical process that employs a cross correlation functionto locate the first landmark 26 a. The cross correlation function sumsthe product of each pixel within the template and a corresponding pixelwithin a search area in digital pattern data 20 where landmark 26 a isexpected to be located. The search process is iterative and correlatesthe template with a number of x,y locations for the stored digitalpattern data. The correlation between a template T placed at (p,q)within the scanned image S is computed as

C(p,q)=ΣT(x,y)*S(x−p, y−q)/K

[0026] for all x,y within the template, and where the normalizingcoefficient K is computed as

K=ΣT(x,y)*T(x,y)

[0027] for all values of x,y within the template. This is done when thetemplate is placed at different p, q values. The range of p, q values touse is determined from a setup file.

[0028] The highest correlation is obtained when the template ispositioned over landmark 26 a, thus determining the position of thefirst landmark 26 a. The position (p,q) where C(p,q) is maximized for agiven search area is used for the midpoint of landmark 26 a. Afterfinding the first landmark 26 a, landmark 26 b and landmark 26 c arelocated in a similar manner.

[0029] Accounting for three landmarks, rather than only two, allowslinear variations in the X and Y directions of scanner 18 to becorrected. Print image analyzer 22 measures the distances betweenlandmarks 26 a, 26 b, and 26 c, compares the actual distances with theideal distances, and calculates X and Y correction factors. Using thesecorrection factors, digital pattern data 20 is “stretched” or “warped.”For example, if the ideal distance between landmark 26 a and landmark 26b were 6000 pixels and the actual measurement was 5900 pixels, a linearscaling in Y of 6000/5900 would correct the scanning error. Thus, usingthe formula Y′=Y * Correction would account for scanner slippage in Y.

[0030] After correcting digital pattern data 20 for scanner error,individual target objects 16 a-f are located using relative positioningtechniques. White space that surrounds the target objects and that isnot part of target objects 16 a-f is removed by dividing digital patterndata 20 into smaller images, one image per target object 16 a-f. Thistechnique of isolating target objects provides a more efficient use ofmemory because pixels representing white space are not stored.

[0031] Relevant physical measurements (referred to as “metrics” herein)known to affect print image characteristics are calculated for eachtarget object 16 a-f. Computing a density distribution for a giventarget object and locating the boundaries of a target object are commontechniques used by several image characteristic measurements carried outby print image analyzer 22. These techniques will be described first.

[0032] A number of metrics require the density distribution for arespective target object 16 a-f. The density distribution can bedepicted as a histogram showing a density range from 0 through 255 alongthe X axis and the number of pixels within each density range measuredalong the Y axis. Generally, values between 0 and 127 are black, andvalues between 128 and 255 are white. Pb(d) and Pw(d) represent thefrequency of black and white pixels, respectively, in a target object.Values for Pb(d) and Pw(d) range from 0.0 to 1.0. The means M_(b) andM_(w) of the black and white distributions are the average black andwhite densities within a black and white target. M_(b) and M_(w) arecomputed as

M _(b) =Σd*Pb(d), for d=0 to 255

M _(w) =Σd(Pw(d), for d=0 to 255

[0033] Variances V_(b) and V_(w), on a scale of 0-255 , of the black andwhite distributions represent how close the densities are to M_(b) andM_(w) within a black or white target.

[0034] V_(b) and V_(w) are computed as

V _(b) =Σd ² *Pw(d)−M _(b) *M _(b), for d=0 to 255

V _(w) =Σd ² *Pw(d)−M _(w)*M_(w), for d=0 to 255

[0035] A number of metrics require determining the location of boundarypixels for a target object and analyzing the pixels near the boundary.An adaptive thresholding technique identifies foreground and backgroundpixels in a target object. The technique establishes a density range forthe foreground and background colors by first computing a densitydistribution for the isolated image. Then, the mean and the variance forthe black and white values is calculated. Densities within 2 or 3standard deviations (calculated by taking the square root of thevariance), of the black mean are considered foreground values. Afterknowing which values are foreground and background values, the targetobject is traced and the boundary of the target object is stored ascoordinate pairs.

[0036] The technique that traces the boundary is known as the Ledley bugfollower and described, for example, in Pratt, W. Digital ImageProcessing, (John Wiley & Sons, N.Y. 1978) which is hereby incorporatedby reference. This technique locates a starting position on the borderof an object and traces the border using a column by column, row by rowsearch for foreground pixels until the original starting position islocated again. A foreground pixel is located by finding the first pixelthat follows three background pixels in a given row. After locating apixel in the boundary of an object, the pixel is stored as an X,Ycoordinate pair, and the technique checks each of the eight neighboringpixels in a counterclockwise fashion for the next boundary pixel,beginning with the pixel immediately to the right of the boundary pixellocated. If a new boundary pixel is detected, that pixel's position ismarked and the search begins again in the same manner. Tracing continuesuntil the technique returns to the initial starting position.

[0037] Referring now to FIG. 3A, components of print image analyzer 22are shown. After the landmark registration process 28 completes, theanalysis software computes a set of metrics and, from the results of themeasurements, generates quality ratings and printer control signals 31,as discussed below. Print image analyzer 22 may also have a database 32that stores historical data regarding the image characteristics ofdigital printer 14 or of other printers of the same model. Thehistorical data is a resource for problems that existed in the past. Forexample, the historical data may include a table that lists problems inone column and solutions to the respective problem in a second column. Asolution can indicate a setting adjustment of digital printer 14.Historical data can also include image characteristics and qualityratings from prior test results that are compared to the current imagecharacteristics and print quality ratings to determine how the qualityhas degraded over time. Print image analyzer 22 may also have a userinterface 34 so that a human can interact with the analysis software.

[0038] Metric Computation

[0039] Referring to FIG. 3B, the metrics computed by the analysissoftware of the print image analyzer are shown. First, print imageanalyzer 22 computes the print density uniformity metrics (step 36),which measure variations in the print density for the foreground and thebackground of the vertical and horizontal solid black target objects 16a, 16 b (shown in FIG. 2) and white target objects 16 c, 16 d (i.e.,white space directly to the left and directly above black target objects16 a, 16 b respectively). Print density uniformity metrics determine theamount of texture in target objects 16 a, 16 b, 16 c, 16 d. The printdensity uniformity metrics calculated are listed in Table 1. TABLE 1Print Density Uniformity Metrics Average Cluster Size Variance ofCluster Size Number and Size of Defects

[0040] Referring now to FIG. 4A, clusters of pixels are illustrated. Acluster is a group of adjacent pixels having a density that falls withina given range. Ideally, there is only one cluster for a given targetobject that was intended to be printed as a uniform density object.Typically however, a target object has several pixel clusters thatconvey texture to the target object. For example, the density range ofpixels in clusters 50 and 51 differ from the density range of pixels incluster 52 and cluster 53.

[0041] Referring now to FIG. 4B, steps used to compute print densityuniformity metrics (step 36) are shown. A boundary trace is performed tolocate the target object (step 56) and a print density range between 0and 255 is selected (step 58). Within the target object, clusters ofpixels having the selected density range are counted (step 60) using arecursive process. The recursive process examines the eight neighboringpixels surrounding a current pixel and marks the neighboring pixel asbelonging to the cluster if the neighboring pixel falls within theselected distribution range. The count is stored in memory (step 62).This is done for different density ranges to identify all clusterswithin the target objects.

[0042] The print density uniformity computations then measure theaverage cluster sizes (i.e., area), the variance of cluster sizes for arange of densities within a selected variation of the mean foregrounddensity (M_(b) and M_(w), as calculated above), and the number and sizesof defects. The smaller the cluster sizes, the less noticeable anytexture will be, although one large cluster would be ideal. The lessvaried the cluster sizes the less noticeable the texture will be.

[0043] Clusters having density values that are 25% or more from the meandensity values are considered defects. A white defect count measures thenumber and size of voids in a black area. A black defect count measuresthe speckles or random black spots found in the white area.

[0044] Referring back to FIG. 3B, the second set of metrics deals with-streaks and smears (step 38). These computations detect streaks andcharacterize their width, frequency, and density. Listed in Table 2 arethe specific metrics and ratings for detecting and characterizingstreaks and smears. TABLE 2 Streaks and Smears Metrics Side to SideUniformity Top to Bottom Uniformity Number of Streaks Width of StreaksPresence of Smearing

[0045] First, to detect vertical streaks and smears a single row ofdensity values that represents the entire horizontal object 16 b iscomputed. This row of values is referred to as a horizontal densityprofile. A single pixel in the horizontal density profile is calculatedby averaging all values for pixels in the same column of target object16 b. Likewise, a vertical density profile for horizontal target object16 a is produced to detect horizontal streaks and smears. A verticaldensity profile takes the average of all pixels in a row to generate asingle value in the vertical profile.

[0046] To measure side-to-side uniformity, a straight line approximationof the horizontal profile is computed using a linear, least-squaresfitting method. Any value more than 1 or 2 standard deviations from themean density is set to the mean density to minimize the effects ofstreaks. The slope of the line measures how uniform the density is fromone side of the target object to the other side of the target object.The density does not vary if the slope is 0. To measure a top-to-bottomuniformity, the same calculations are carried out on the verticalprofile.

[0047] Referring now to FIG. 4C, the presence of streaks is detected bycross correlating a Gaussian function 64 that models a streak densityagainst the profile 66 (either the horizontal or vertical profile). Theresult of the cross correlation for different positions of the model isstored in an array that is indexed by position. The number of streaksdetected and their perceptible visibility are determined by ranking thecorrelation results and finding those with high correlation values. Thewidth of the streak is established by cross-correlating the profile withstreak models having different widths and finding the best match (i.e.,the highest correlation value).

[0048] Referring now to FIG. 4D, toner smearing is detected by fittingthe horizontal and vertical profile values to a higher order polynomialequation, such as

D(x)=ax ³ +bx ² +cx=d

[0049] where D is the density value, x is the position or column indexwithin one row, and a, b, c, and d are coefficients of theapproximation. Toner smearing could also be detected by approximatingthe profile as a Fourier Series to measure the degree that the printdensity deviates from the ideal case of a straight line. When using apolynomial approximation, the second and third order coefficientsindicate the degree the profile deviates from a straight line, whichwould be the first order coefficient. The sum of the absolute value ofthe magnitude of all non-linear coefficients can be used as anindication of how much the profile deviates from an ideal case.

[0050] Referring back to FIG. 3B, the third set of metrics 40 measurepositional accuracy by analyzing the consistency by which imaged dots onthe printed page are placed in the vertical and horizontal directions.Horizontal positional accuracy is the difference between an actualhorizontal position and the expected horizontal position for a verticalline. Vertical positional accuracy is the difference between an actualvertical position and the expected vertical position for a horizontalline. The vertical and horizontal lines in target objects 16 f are usedfor this test. In addition to horizontal and vertical accuracy, whichdetect jitter, the metric determines the deviation from 90° between ahorizontal and a vertical line. Table 3 summarizes the specific metricsand ratings for positional accuracy. TABLE 3 Positional Accuracy MetricsAverage Deviation from Horizontal Line Average Deviation from VerticalLine Deviation from 90°

[0051] Positional accuracy metrics also use target objects 16 e, whichare groups of solid black squares having varying sizes and spacesbetween squares. Target objects 16 e are used to determine jitter andskew.

[0052] In calculating the positional accuracy metrics, a boundary traceof the target object (using the medley bug follower previous described)is first carried out. To determine horizontal accuracy, a measurement ofthe difference in the vertical position of a horizontal line overseveral rows is taken. Vertical accuracy is measured by taking thedifference in the horizon position of a vertical line over severalcolumns. Another method for determining vertical and horizontal jittergenerates straight line approximations, using a minimum least squaresfitting technique, for positions in square target objects and computesthe average vertical and horizontal deviation. For a given straight lineapproximation, the normal distance between each edge point and astraight line is computed using a vector cross-product.

[0053] The amount by which adjacent edges of the target deviate from 90degrees is also measured to determine the amount of skew. The formulathat determines the amount of skew uses simple trigonometry and theslopes of the straight line approximations for two adjacent sides of thetarget object. The equation that determines the angle between the lineapproximation and the x-axis is

θ=arctan (ΔY/ΔX)

[0054] Referring once more to FIG. 3B, the fourth set of metrics measureedge sharpness (step 42). Table 4 summarizes the specific metrics andratings for edge sharpness. TABLE 4 Edge Sharpness Metrics EdgeUniformity Edge Blur

[0055] Referring to FIG. 2, edge sharpness metrics use target objects 16e, which are groups of solid block squares having varying sizes andspaces between squares. The blocks vary in size from 1×1 pixel to 60×60pixels.

[0056] Edge sharpness is computed by examining an edge density profilefor pixels immediately outside a boundary and computing how close theprofile approximates an ideal edge. An edge profile is obtained byaveraging the print density of all pixels located one pixel from theboundary, then averaging the print density of all pixels located twopixels from the boundary, and so on up to a predetermined number such asfive pixels from the boundary. Each average becomes one value in theprofile. From the edge profile, metrics for edge uniformity and edgeblur are computed.

[0057] Edge uniformity measures the variance of print density whentransitioning from the foreground to the background. The measurementsums the variances in the edge profile values. The smaller the sum ofthe variances is for a given range of pixels, the sharper the edgeappears, whereas the larger the sum, the more ragged and blurred theedge appears. A weighted sum of the variances can be used to accentuatethe importance of pixels close to the edge versus those farther from theedge.

[0058] Edge blur measures how close the edge profile approximates theideal edge. The ideal edge profile transitions from solid black to whitein one or two pixels, almost in a straight-line fashion. Edge blurapproximates the edge profile as a straight line and uses the slope ofthe line approximation as an indication of blur, where the higher theslope the sharper the image and the lower the slope, the longer thetransition from black to white and the more blurred the edge appears.

[0059] Referring once more to FIG. 3B, the fifth metric computes edgeacuity (step 44). Edge acuity measures the ability of digital printer 14to reproduce detail. The metric has only one component, as shown inTable 5. TABLE 5 Edge Acuity Metric Edge Acuity

[0060] Referring to FIG. 2, target objects 16 f are horizontal,vertical, and 45° lines used for determining edge acuity. The edgeacuity metric finds the closest separable line pairs within targetobjects having pairs of lines separated by varying distances. Thespacing between the lines are increased by one printer unit for everyline pair. The thickness of the lines can be held constant or varied. Toaccount for imaging capabilities in the horizontal and verticaldirections, the test uses three sets of target objects, one withvertical lines, one with horizontal lines, and one with 45° lines.

[0061] The edge acuity metric (step 44) locates the lines and computesthe distance between the opposing edges of the closest lines. If aseparation between the lines is detected, the distance is consideredresolvable. When the line pair distances drops below the resolvablecapability of the printer, all lines fuse into one object and areconsidered as one object. This is detected because the size of theidentified object is greater than the size of the target (i.e., theidentified object would include two or more targets).

[0062] Quality Ratings and Printer/Control Signal Generation

[0063] Quality ratings and printer control signals are generated fromthe metrics in process 31 (FIG. 3A). The quality ratings include anoverall quality rating and more specific quality ratings, for examplewith respect to print density uniformity, streaks, smears, positionalaccuracy, edge sharpness, and edge acuity. To generate the variousquality ratings, first the metric values in Tables 1-5 are normalized tovalues between 0.0 and 1.0 and the weights are applied to the normalizedvalues. For example, a lower natural limit for a metric would be 0.0,and an upper natural limit would be 1.0.

[0064] A weight is assigned to each physical measurement listed inTables 1-5 and defines the relative significance of each measurement tothe respective quality rating. The weight is multiplied by thenormalized quality rating of the respective test. The overall qualityrating is the sum of all the weighted and normalized quality ratings forthe individual tests.

[0065] Using the quality ratings, print image analyzer 22 can identifyfactors responsible for a quality deviation and suggest an appropriatecorrective action to tune and calibrate digital printer 14 to anacceptable quality level. For example, an unacceptable rating for thepresence of smears may indicate that the toner cartridge needsreplacing. Furthermore, ratings and weights given to the measurementscan depend on the age and model of the digital printing device.

[0066] Other embodiments are within the scope of the following claims.For example, print image analyzer 22 can be part of the controllerfirmware of digital printer 14. This embodiment enables digital printer14 to directly measure its own output quality. FIG. 5 shows scanner 18built into the output path of digital printer 14 and receiving testpages as needed from printing drum 15. Each time digital printer 14 runsdiagnostics, printed image 16 would be printed, scanned, and comparedwith previous measurements. If the print quality degraded below athreshold or a serious problem were detected, the operator would bealerted by an alarm or light indicator.

[0067] In an alternative embodiment, such as shown in FIG. 6, printimage analyzer 22 can be integrated into an image quality assurancesystem for printers in a distributed system. The results of performingthe analysis could be stored locally with each printer or centrally inan administrative site.

[0068] In yet another embodiment, print image analyzer 22 can beintegrated into a service kit used by a technician to measure imagecharacteristic deviations and determine appropriate corrections. Adatabase could track problems and corrections that correlate withdifferent types of measurements taken from the test sample. A left edgesmudge, for example, might correlate with a misaligned toner roller. Theinitial scan of the printed page provides the technician with a list ofpotential problems and likely corrections. Once the problem isidentified and corrected, the technician updates the database with theinformation.

[0069] Print image analyzer 22 can be integrated into a printer's testfixture to provide continuous on-line measurements of print imagecharacteristics. This would be especially useful for high volumeprinters that require large volumes of output to show significantquality changes. The ability to continuously sample the print output,plot variations, and correlate those results enables a printermanufacturer to predict quality degradation and thus reduce testingcosts.

[0070] Print image analyzer 22 can also be used to provide on-linemetrics for a printer manufacturer's quality assurance or research anddevelopment departments.

[0071]FIG. 6 shows a computer system 100 suitable for supporting one ormore print image analyzers 102. The computer system 100 includes adigital computer 104, a display monitor 106, a keyboard 108, a mouse orother pointing device 110, and a mass storage device 112 (e.g., harddisk drive, magneto-optical disk drive, or floppy disk drive). Thecomputer 104 includes memory 120, a processor 122, and other customarycomponents, such as, memory bus and is peripheral bus (not shown). Thecomputer 104 has a network interface 124 to communicate with remotecomputer systems. Digital printer 14 and scanner 18 also connect tonetwork interface 124.

[0072] In place of bitmaps, the test pattern data can be in the form ofpage description language such as PostScript or HP's printer controllanguage (PCL).

What is claimed is:
 1. A method of measuring image characteristics ofprinted output from a digital printer, comprising: sending test patterndata to a digital printer, said test pattern including one or moretarget objects designed to reveal one or more specific printed imagecharacteristics; generating a printed image of said test pattern data atsaid digital printer; scanning said printed image to obtain digitalpattern data; and analyzing said digital pattern data to generate one ormore quantitative ratings with respect to said one or more printed imagecharacteristics.
 2. The method of claim 1 , wherein a printed testpattern is scanned prior to sending said test pattern data to saiddigital printer.
 3. The method of claim 1 , wherein said analyzingincludes generating a quality rating related to image quality from saidone or more quantitative ratings.
 4. The method of claim 3 , whereinsaid quality rating is based on a human perception of quality.
 5. Themethod of claim 1 , further comprising: indicating unacceptable printquality in response to said one or more quantitative ratings.
 6. Themethod of claim 1 , further comprising: indicating a setting adjustmentto said digital printer in response to said one or more quantitativeratings.
 7. The method of claim 1 , further comprising: automaticallyadjusting a setting to said digital printer in response to said one ormore quantitative ratings.
 8. The method of claim 1 , wherein saidanalyzing step evaluates said one or more quantitative ratings withrespect to historical data.
 9. The method of claim 1 , wherein saidquantitative ratings include a physical measurement of said one or moreobjects in said digital pattern data.
 10. The method of claim 3 ,wherein said scanner has higher resolution than said digital printer.11. The method of claim 9 , wherein landmarks in said digital patterndata are used to identify positions for said one or more target objects.12. The method of claim 11 , wherein said landmarks are located in thecorners of said digital pattern data.
 13. The method of claim 11 ,wherein said landmarks are used to adjust for scanner deviations. 14.The method of claim 9 , wherein said physical measurement determinesdensity uniformity.
 15. The method of claim 9 , wherein said physicalmeasurement determines positional accuracy.
 16. The method of claim 9 ,wherein said physical measurement determines edge sharpness.
 17. Themethod of claim 9 , wherein said physical measurement determines edgeacuity.
 18. The method of claim 9 , wherein said physical measurementdetects the presence of streaks and smears.
 19. The method of claim 1 ,wherein said analyzing step produces a plurality of physicalmeasurements of said digital pattern data.
 20. The method of claim 19 ,further comprising: combining said physical measurements into a qualityrating.
 21. The method of claim 20 , wherein said combining stepcomprises: producing a plurality of weights, each weight assigned to onesaid physical measurement; normalizing said physical measurements;computing said quality rating by multiplying each said weight by therespective said normalized physical measurement; and summing theproducts of said weights and said measurements into one overall qualityrating.
 22. The method of claim 1 , wherein said analyzing stepidentifies a plurality of factors responsible for quality deviation andsuggests appropriate corrective action.
 23. The method of claim 1 ,wherein said analyzing step uses a database with historical data uniquefor the digital printer.
 24. The method of claim 1 , wherein saidanalyzing step uses a database with quality data correlated with printersetting adjustments.
 25. The method of claim 1 , wherein said testpattern data includes samples of horizontal and vertical lines.
 26. Themethod of claim 25 , wherein said horizontal and vertical lines areseparated by different distances.
 27. The method of claim 1 , whereinthe test pattern data is stored as a bitmap data or represented as apage in a page description language.
 28. A computer program, residing ona computer-readable medium, comprising instructions causing a printanalyzer system to: produce digital test pattern data, said test patternincluding one or more target objects designed to reveal one or morespecific printed image characteristics; send said digital test patterndata to a digital printer to generate a printed image at said digitalprinter; receive digital pattern data generated from scanning saidprinted image; and analyze said digital pattern data to generate one ormore quantitative ratings with respect to said one or more printed imagecharacteristics.
 29. The computer program of claim 28 , whereinanalyzing of said digital pattern data includes generating a qualityrating related to image quality from said one or more quantitativeratings.
 30. The computer program of claim 28 , further comprisinginstructions causing a print analyzer system to: indicate a settingadjustment to said digital printer in response to said one or morequantitative ratings.
 31. The computer program of claim 28 , furthercomprising instructions causing a print analyzer system to:automatically adjust a setting to said digital printer in response tosaid one or more quantitative ratings.
 32. The computer program of claim28 , wherein said quantitative ratings include a physical measurement ofsaid one or more objects in said digital pattern data.
 33. The computerprogram of claim 28 , wherein analyzing of said digital pattern dataproduces a plurality of physical measurements of said digital patterndata and further comprising instructions to cause a print analyzersystem to combine said physical measurements into a quality rating. 34.The computer program of claim 28 , wherein said digital pattern data hasa higher resolution than said printed image.
 35. The computer program ofclaim 28 , further comprising instructions causing a print analyzersystem to: read historical data in a database; and evaluate said one ormore quantitative ratings with said historical data.
 36. A printanalyzer system for measuring image characteristics of printed outputfrom a digital printer, comprising: a source of digital pattern data,said test pattern including one or more target objects designed toreveal one or more specific printed image characteristics; a digitalprinter that receives said digital pattern data as input and outputs aprinted image; a scanner that receives said printed image as input andoutputs digital pattern data having higher resolution than said printedimage; and an image quality analyzer receiving said digital pattern dataand analyzing said digital pattern data to generate one or morequantitative ratings with respect to one or more printed imagecharacteristics.
 37. The print analyzer system of claim 36 , furthercomprising: a database with historical data for said digital printer.38. The print analyzer system of claim 36 , further comprising: adatabase with quality data correlated with printer setting adjustments.39. A digital printer for measuring image characteristics of printedoutput from said digital printer, comprising: a print engine to receivetest pattern data and generate a printed sheet having a printed imagethat is delivered to a routing path, said test pattern including one ormore target objects designed to reveal one or more specific printedimage characteristics; and an integral scanner that interacts with saidrouting path to scan an image on said printed sheet and generatesdigital pattern data, said scanner having a higher resolution than saidprint engine.
 40. The digital printer of claim 39 , further comprising:an analyzer included in a digital printer controller of said printengine, said analyzer receiving said digital pattern data and analyzingsaid digital pattern data to generate one or more quantitative ratingswith respect to said one or more printed image characteristics.
 41. Thedigital printer of claim 39 , further comprising: an indicator toindicate unacceptable print quality in response to said one or morequantitative ratings.
 42. The digital printer of claim 39 , furthercomprising: a setting adjustment indicator to respond to said one ormore quantitative ratings.
 43. The digital printer of claim 39 , furthercomprising: an automatic print setting adjustment in response to saidone or more quantitative ratings.
 44. A test instrument for measuringimage characteristics of printed output from a digital printer,comprising: an input that receives printed pattern data based onscanning an image printed by a digital printer in response to testpattern data, said test pattern including one or more target objectsdesigned to reveal one or more specific printed image characteristics;an analyzer to receive said digital pattern data and analyze saiddigital pattern data to generate one or more quantitative ratings withrespect to said one or more printed image characteristics; and an outputresponding to said one or more quantitative ratings.
 45. The testinstrument of claim 44 , wherein said analyzer identifies factorsresponsible for quality deviation.
 46. The test instrument of claim 44 ,wherein said analyzer generates a quality rating related to imagequality from said one or more quantitative ratings.
 47. The testinstrument of claim 44 , further comprising: a setting adjustmentindicator to respond to said one or more quantitative ratings.
 48. Thetest instrument of claim 44 , wherein said output automatically adjustsa setting to said digital printer.
 49. The test instrument of claim 44 ,wherein said quantitative ratings include a physical measurement of saidone or more objects in said digital pattern data.
 50. The testinstrument of claim 44 , wherein said analyzer produces a plurality ofphysical measurements of said digital pattern data and combines saidphysical measurements into a quality rating.
 51. The test instrument ofclaim 44 , wherein said analyzer suggests appropriate corrective action.52. The test instrument of claim 44 , further comprising: a databasehaving historical data for said digital printer.
 53. The test instrumentof claim 44 , further comprising: a database having quality datacorrelated with said digital printer setting adjustments.